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@smartjourneymining

Smart Journey Mining

Research project to support successful digitalisation by extending the research front in process mining and service science with formal analytics and AI.

Although digital services are supposed to simplify our lives and increase our efficiency, they often frustrate and burden customers, users, and employees. The Smart Journey Mining (SJM) vision is to increase the quality of services by uniting research on customer journeys and process mining using new developments in logic-based analysis and artificial intelligence.

Based on user journeys we will analyse, model, and observe how humans experience digital services, rather than taking the perspective of service providers and service systems. The main source of data comes from service providers, but also from simulated user journeys. We will trace data left from users in various systems during repeated interactions with a service over time, on the level of individuals.

SJM will provide researchers and analysts the necessary methods and tools to work across backend systems and detect patterns in vast volumes of user data. This will inform novel guidelines for the successful digitalisation of services. The SJM results will be made available to academia, industry and the public sector, thus supporting improved service quality in our society.

This project has received funding from the Research Council of Norway

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  1. User-Journey-Games User-Journey-Games Public

    Repository for game theory approach for journeys

    Jupyter Notebook 2

  2. CJML CJML Public

    JavaScript 1

  3. SJM-Integrator SJM-Integrator Public

    Smart Journey Integrator - A tool to map event data to CJML in an Event Knowledge Graph

    Python 1

  4. probabilistic_games probabilistic_games Public

    Jupyter Notebook

Repositories

Showing 10 of 10 repositories
  • SJM-Integrator Public

    Smart Journey Integrator - A tool to map event data to CJML in an Event Knowledge Graph

    smartjourneymining/SJM-Integrator’s past year of commit activity
    Python 1 0 0 0 Updated Nov 28, 2024
  • smartjourneymining/probabilistic_games’s past year of commit activity
    Jupyter Notebook 0 GPL-2.0 0 0 0 Updated Sep 18, 2024
  • bpi_games Public

    Repository for BPI Games Idea

    smartjourneymining/bpi_games’s past year of commit activity
    Jupyter Notebook 2 0 0 0 Updated Aug 3, 2024
  • smartjourneymining/spotify_journey’s past year of commit activity
    Jupyter Notebook 0 0 0 0 Updated Mar 11, 2024
  • User-Journey-Games Public

    Repository for game theory approach for journeys

    smartjourneymining/User-Journey-Games’s past year of commit activity
    Jupyter Notebook 2 0 0 0 Updated Feb 15, 2024
  • abs_journeys_aol-23 Public

    User journey games modeled in ABS

    smartjourneymining/abs_journeys_aol-23’s past year of commit activity
    Python 0 0 0 0 Updated May 30, 2023
  • dgl Public Forked from ketyi/dgl

    Python package built to ease deep learning on graph, on top of existing DL frameworks.

    smartjourneymining/dgl’s past year of commit activity
    Python 0 Apache-2.0 3,045 0 0 Updated Sep 29, 2022
  • sequential-deep-learning-models Public

    The framework implements in a coherent manner several state-of-the-art sequential deep learning approaches (LSTM, Seq-AE, Seq-AE-GAN, Transformer, BERT, GPT, WaveNet) for process prediction. These are implemented in Python and PM4Py and provide a starting point for process prediction.

    smartjourneymining/sequential-deep-learning-models’s past year of commit activity
    Python 7 0 0 0 Updated Sep 24, 2022
  • .github Public
    smartjourneymining/.github’s past year of commit activity
    0 0 0 0 Updated Sep 14, 2022
  • CJML Public
    smartjourneymining/CJML’s past year of commit activity
    JavaScript 0 1 0 1 Updated May 17, 2022

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